Discussion
Insecticide resistance monitoring is the key to sustain
insecticide-mediated control efficiency. Molecular detecting assays can
be used to detect resistant markers accurately at early stages to avoid
resistance evolution (Network, 2016). Target-site resistance, which is
mainly caused by target insensitive mutations, and metabolic resistance,
which is mainly caused by overexpressed detoxification genes, are the
two main mechanisms of insecticide resistance (Ffrench-Constant, 2013).
The detection of these two kinds of resistance can well reveal the
mechanism of resistance of insect pest populations. PCR-based
target-site mutation detection assays rely on genotyping individuals one
by one within an insect pest population and are not only time-consuming,
but also result in a high false-positive rate (Hirayama et al., 2010;
Blais et al., 2015). The DNA microarray which used to detect
differentially expressed detoxification genes are inefficient and
complex, because of the demand for prerequisite knowledge of the
reference sequences, low resolution of expression level, and
background signals (Kogenaru, Qing, Guo, & Wang, 2012; Mantione et al.,
2014). RNA-Seq sequences the transcription products of pooled samples
of insect pest populations and can obtain the SNP information in gene
expressed regions as well as provide gene expression level comparison
(De Wit et al., 2015). More and more researchers have adopted RNA-Seq as
a method to study resistance mechanisms and detect resistant markers
(David et al., 2014; Faucon et al., 2017; Mamidala et al., 2012).
Here, we developed FastD to detect the target insensitive mutations and
overexpressed detoxification genes. By collecting insensitive mutations
on four kinds of insecticide targets and resistance-associated gene
sequences of 82 insect species, the FastD program can be applied to
detect resistant markers of a wide-range of species. The webserver of
the FastD program uses SAM files as input and can analyze the samples
more quickly than traditional methods such as PCR or microarrays. With
these characteristics, FastD program offers a wide range of applications
and great value.
As a proof of concept, FastD program was used to detect the
resistance-associated markers of two insects, P. xylostella andA. gossypii . The resistance of insect populations can be well
estimated by these resistant markers via FastD program. The RyRmutation G4946E and CYP6BG1 gene overexpression have also been
reported to be associated with resistance to chlorantraniliprole (Guo,
Liang, Zhou, & Gao, 2014; X. Li et al., 2018). Interestingly, The
resistance level of CHR population with higher G4946E frequency
(94.55%) is higher than ZZ population with lower G4946E frequency
(66.1%) and six overexpressed detoxification genes. We speculated that
G4946E may play a dominant role in resistance or there are other
mechanisms conferring resistance in these resistant populations. In
addition, 40 resistant allele reads among 575 all allele reads
were detected in susceptible CHS population. We speculated that there
may be few resistant individuals in CHS population. The discrepancy need
further investigation. The nAChR beta1 subunit mutation R81T (Koichi
Hirata et al., 2015) and overexpression of CYP6CY22 andCYP6CY13 genes have been reported to be associated with
resistance to neonicotinoids. Moreover, four genes overexpressed in theP. xylostella ZZ population and seven genes overexpressed in theA. gossypii KR population which were not reported before are
worth further study, indicating the value of FastD as a tool for both
confirmation of resistance and discovery of new resistance mechanisms.
As a tool to detect resistant markers to monitor the emergence and
development of insecticide resistance from RNA-Seq data, there are still
some limitations. We plan to improve the following areas in the future.
First, insecticide resistance with the polygene inheritance model is
also associated with other important mechanisms, especially the
detoxification gene amplification. Due to the limitation of RNA-Seq
technique, gene amplification can’t be identified by FastD-MR. We plan
to add new function to identify gene amplification based on genome
resequencing data. Second, the accuracy of mutation frequency
calculated by FastD-TR is limited by the fact that RNA-Seq reads from
pooled sample have potentially different levels of contribution from
each insect sample and allele. Therefore, we recommend users to use
larger number of individuals sampled in pool to get more accurate
result. Third, the resistance level is determined empirically based on
detected resistant markers by the FastD program. More quantitative
relationships between the resistant markers and resistance are critical
and could be established with machine learning methods. Fourth, aside
from insecticide resistance, resistance in other pests (herbicide
resistance and fungicide resistance) are also associated with target
insensitive mutations and overexpressed detoxification genes (Bohnert et
al., 2019; Q. Li et al., 2013). Estimating the resistance to herbicide
and fungicide will be added in the next version of FastD program.